Overview

Dataset statistics

Number of variables15
Number of observations163643
Missing cells279
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 MiB
Average record size in memory120.0 B

Variable types

Text1
Categorical2
Numeric10
DateTime2

Alerts

type_de_station has constant value "ISS"Constant
direction_du_vecteur_de_vent_max is highly overall correlated with direction_du_vecteur_de_vent_max_en_degres and 1 other fieldsHigh correlation
direction_du_vecteur_de_vent_max_en_degres is highly overall correlated with direction_du_vecteur_de_vent_max and 1 other fieldsHigh correlation
direction_du_vecteur_vent_moyen is highly overall correlated with direction_du_vecteur_de_vent_max and 1 other fieldsHigh correlation
force_moyenne_du_vecteur_vent is highly overall correlated with force_rafale_maxHigh correlation
force_rafale_max is highly overall correlated with force_moyenne_du_vecteur_ventHigh correlation
humidite is highly overall correlated with temperature_en_degre_cHigh correlation
pluie is highly overall correlated with pluie_intensite_maxHigh correlation
pluie_intensite_max is highly overall correlated with pluieHigh correlation
temperature_en_degre_c is highly overall correlated with humiditeHigh correlation
id is highly imbalanced (99.6%)Imbalance
pluie is highly skewed (γ1 = 35.4268385)Skewed
data has unique valuesUnique
humidite has 1899 (1.2%) zerosZeros
direction_du_vecteur_de_vent_max has 144360 (88.2%) zerosZeros
pluie_intensite_max has 158790 (97.0%) zerosZeros
direction_du_vecteur_vent_moyen has 150370 (91.9%) zerosZeros
pluie has 159586 (97.5%) zerosZeros
direction_du_vecteur_de_vent_max_en_degres has 144360 (88.2%) zerosZeros
force_moyenne_du_vecteur_vent has 120805 (73.8%) zerosZeros
force_rafale_max has 74100 (45.3%) zerosZeros

Reproduction

Analysis started2026-01-13 21:48:59.240804
Analysis finished2026-01-13 21:49:15.830030
Duration16.59 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

data
Text

Unique 

Distinct163643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:15.977077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.998802
Min length1

Characters and Unicode

Total characters3927236
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163643 ?
Unique (%)100.0%

Sample

1st row552a630fca7800000c200000
2nd row55296af0d61000000c600000
3rd row55296b509a3800000c600000
4th row55296b30a63000000c600000
5th row552a63f00e6800000c200800
ValueCountFrequency (%)
552a630fca7800000c2000001
 
< 0.1%
55296af0d61000000c6000001
 
< 0.1%
55296b509a3800000c6000001
 
< 0.1%
55296b30a63000000c6000001
 
< 0.1%
552a63f00e6800000c2008001
 
< 0.1%
55296b90964000000c600c001
 
< 0.1%
55296bd08a4000000c6000001
 
< 0.1%
552a60b0226800000c4000001
 
< 0.1%
552c35f3a55000004c0018001
 
< 0.1%
552a6b310df000000c800c001
 
< 0.1%
Other values (163633)163633
> 99.9%
2026-01-13T22:49:16.135128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01633534
41.6%
5378270
 
9.6%
4244289
 
6.2%
8219602
 
5.6%
2200782
 
5.1%
c170080
 
4.3%
1163157
 
4.2%
6149864
 
3.8%
d131979
 
3.4%
e121793
 
3.1%
Other values (9)513886
 
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3927236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01633534
41.6%
5378270
 
9.6%
4244289
 
6.2%
8219602
 
5.6%
2200782
 
5.1%
c170080
 
4.3%
1163157
 
4.2%
6149864
 
3.8%
d131979
 
3.4%
e121793
 
3.1%
Other values (9)513886
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3927236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01633534
41.6%
5378270
 
9.6%
4244289
 
6.2%
8219602
 
5.6%
2200782
 
5.1%
c170080
 
4.3%
1163157
 
4.2%
6149864
 
3.8%
d131979
 
3.4%
e121793
 
3.1%
Other values (9)513886
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3927236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01633534
41.6%
5378270
 
9.6%
4244289
 
6.2%
8219602
 
5.6%
2200782
 
5.1%
c170080
 
4.3%
1163157
 
4.2%
6149864
 
3.8%
d131979
 
3.4%
e121793
 
3.1%
Other values (9)513886
 
13.1%

id
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing19
Missing (%)< 0.1%
Memory size1.2 MiB
42.0
163534 
1.0
 
89
0.0
 
1

Length

Max length4
Median length4
Mean length3.99945
Min length3

Characters and Unicode

Total characters654406
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row42.0
2nd row42.0
3rd row42.0
4th row42.0
5th row42.0

Common Values

ValueCountFrequency (%)
42.0163534
99.9%
1.089
 
0.1%
0.01
 
< 0.1%
(Missing)19
 
< 0.1%

Length

2026-01-13T22:49:16.180142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T22:49:16.218762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
42.0163534
99.9%
1.089
 
0.1%
0.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0163625
25.0%
.163624
25.0%
4163534
25.0%
2163534
25.0%
189
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)654406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0163625
25.0%
.163624
25.0%
4163534
25.0%
2163534
25.0%
189
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)654406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0163625
25.0%
.163624
25.0%
4163534
25.0%
2163534
25.0%
189
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)654406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0163625
25.0%
.163624
25.0%
4163534
25.0%
2163534
25.0%
189
 
< 0.1%

humidite
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean64.819463
Minimum0
Maximum97
Zeros1899
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.256799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q153
median68
Q380
95-th percentile89
Maximum97
Range97
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.035528
Coefficient of variation (CV)0.29366994
Kurtosis0.43118706
Mean64.819463
Median Absolute Deviation (MAD)13
Skewness-0.79415647
Sum10605955
Variance362.35133
MonotonicityNot monotonic
2026-01-13T22:49:16.302800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874414
 
2.7%
863911
 
2.4%
883855
 
2.4%
853796
 
2.3%
673701
 
2.3%
683690
 
2.3%
663641
 
2.2%
693597
 
2.2%
813370
 
2.1%
653363
 
2.1%
Other values (82)126285
77.2%
ValueCountFrequency (%)
01899
1.2%
76
 
< 0.1%
811
 
< 0.1%
910
 
< 0.1%
1043
 
< 0.1%
1147
 
< 0.1%
1270
 
< 0.1%
1382
 
0.1%
1484
 
0.1%
15110
 
0.1%
ValueCountFrequency (%)
976
 
< 0.1%
96155
 
0.1%
95443
 
0.3%
94502
 
0.3%
93841
 
0.5%
921431
 
0.9%
911984
1.2%
902427
1.5%
893086
1.9%
883855
2.4%

direction_du_vecteur_de_vent_max
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.97342061
Minimum0
Maximum15
Zeros144360
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.339801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9202603
Coefficient of variation (CV)2.9999985
Kurtosis7.3728472
Mean0.97342061
Median Absolute Deviation (MAD)0
Skewness2.9522134
Sum159274
Variance8.5279205
MonotonicityNot monotonic
2026-01-13T22:49:16.375801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0144360
88.2%
92730
 
1.7%
102524
 
1.5%
112387
 
1.5%
122039
 
1.2%
81929
 
1.2%
71517
 
0.9%
31378
 
0.8%
21329
 
0.8%
4769
 
0.5%
Other values (6)2661
 
1.6%
ValueCountFrequency (%)
0144360
88.2%
1502
 
0.3%
21329
 
0.8%
31378
 
0.8%
4769
 
0.5%
5428
 
0.3%
6450
 
0.3%
71517
 
0.9%
81929
 
1.2%
92730
 
1.7%
ValueCountFrequency (%)
15223
 
0.1%
14379
 
0.2%
13679
 
0.4%
122039
1.2%
112387
1.5%
102524
1.5%
92730
1.7%
81929
1.2%
71517
0.9%
6450
 
0.3%

pluie_intensite_max
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.0063682979
Minimum0
Maximum2.6
Zeros158790
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.423322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.6
Range2.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.041509295
Coefficient of variation (CV)6.5181145
Kurtosis463.34339
Mean0.0063682979
Median Absolute Deviation (MAD)0
Skewness14.201237
Sum1042
Variance0.0017230216
MonotonicityNot monotonic
2026-01-13T22:49:16.461363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0158790
97.0%
0.24675
 
2.9%
0.482
 
0.1%
0.625
 
< 0.1%
0.822
 
< 0.1%
19
 
< 0.1%
1.26
 
< 0.1%
1.66
 
< 0.1%
23
 
< 0.1%
1.42
 
< 0.1%
Other values (2)3
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0158790
97.0%
0.24675
 
2.9%
0.482
 
0.1%
0.625
 
< 0.1%
0.822
 
< 0.1%
19
 
< 0.1%
1.26
 
< 0.1%
1.42
 
< 0.1%
1.66
 
< 0.1%
23
 
< 0.1%
ValueCountFrequency (%)
2.61
 
< 0.1%
2.22
 
< 0.1%
23
 
< 0.1%
1.66
 
< 0.1%
1.42
 
< 0.1%
1.26
 
< 0.1%
19
 
< 0.1%
0.822
 
< 0.1%
0.625
 
< 0.1%
0.482
0.1%

pression
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99942.995
Minimum90000
Maximum102500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.508386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90000
5-th percentile98500
Q199800
median100200
Q3100600
95-th percentile101400
Maximum102500
Range12500
Interquartile range (IQR)800

Descriptive statistics

Standard deviation1780.1065
Coefficient of variation (CV)0.017811219
Kurtosis22.53986
Mean99942.995
Median Absolute Deviation (MAD)400
Skewness-4.5036494
Sum1.6352973 × 1010
Variance3168779.3
MonotonicityNot monotonic
2026-01-13T22:49:16.564923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10010010752
 
6.6%
10030010511
 
6.4%
10020010198
 
6.2%
10040010167
 
6.2%
10050010144
 
6.2%
1000009747
 
6.0%
999008910
 
5.4%
1006008417
 
5.1%
998007562
 
4.6%
1007007410
 
4.5%
Other values (50)69805
42.7%
ValueCountFrequency (%)
900004272
2.6%
967007
 
< 0.1%
9680020
 
< 0.1%
9690045
 
< 0.1%
9700027
 
< 0.1%
9710058
 
< 0.1%
9720045
 
< 0.1%
9730061
 
< 0.1%
97400103
 
0.1%
97500118
 
0.1%
ValueCountFrequency (%)
10250012
 
< 0.1%
10240065
 
< 0.1%
10230050
 
< 0.1%
102200229
 
0.1%
102100360
 
0.2%
102000383
 
0.2%
101900906
0.6%
101800998
0.6%
101700964
0.6%
1016001388
0.8%

direction_du_vecteur_vent_moyen
Real number (ℝ)

High correlation  Zeros 

Distinct181
Distinct (%)0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.306632
Minimum0
Maximum360
Zeros150370
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.617950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile118
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.850456
Coefficient of variation (CV)3.9694416
Kurtosis19.522653
Mean12.306632
Median Absolute Deviation (MAD)0
Skewness4.3860378
Sum2013648
Variance2386.3671
MonotonicityNot monotonic
2026-01-13T22:49:16.674803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0150370
91.9%
224629
 
0.4%
202618
 
0.4%
66542
 
0.3%
246537
 
0.3%
44467
 
0.3%
180424
 
0.3%
90327
 
0.2%
156301
 
0.2%
270234
 
0.1%
Other values (171)9174
 
5.6%
ValueCountFrequency (%)
0150370
91.9%
2108
 
0.1%
493
 
0.1%
6100
 
0.1%
868
 
< 0.1%
10109
 
0.1%
1273
 
< 0.1%
1481
 
< 0.1%
1670
 
< 0.1%
1879
 
< 0.1%
ValueCountFrequency (%)
36063
< 0.1%
35817
 
< 0.1%
35646
< 0.1%
35441
< 0.1%
35244
< 0.1%
35028
< 0.1%
34834
< 0.1%
34628
< 0.1%
34421
 
< 0.1%
34220
 
< 0.1%

type_de_station
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Memory size1.2 MiB
ISS
163623 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters490869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowISS
2nd rowISS
3rd rowISS
4th rowISS
5th rowISS

Common Values

ValueCountFrequency (%)
ISS163623
> 99.9%
(Missing)20
 
< 0.1%

Length

2026-01-13T22:49:16.728833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-13T22:49:16.755456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
iss163623
100.0%

Most occurring characters

ValueCountFrequency (%)
S327246
66.7%
I163623
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)490869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S327246
66.7%
I163623
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)490869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S327246
66.7%
I163623
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)490869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S327246
66.7%
I163623
33.3%

pluie
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.010161163
Minimum0
Maximum11.6
Zeros159586
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.785498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11.6
Range11.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11139461
Coefficient of variation (CV)10.962782
Kurtosis2171.5337
Mean0.010161163
Median Absolute Deviation (MAD)0
Skewness35.426838
Sum1662.6
Variance0.01240876
MonotonicityNot monotonic
2026-01-13T22:49:16.832550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0159586
97.5%
0.22566
 
1.6%
0.4703
 
0.4%
0.6296
 
0.2%
0.8179
 
0.1%
189
 
0.1%
1.247
 
< 0.1%
1.434
 
< 0.1%
1.627
 
< 0.1%
1.819
 
< 0.1%
Other values (23)77
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0159586
97.5%
0.22566
 
1.6%
0.4703
 
0.4%
0.6296
 
0.2%
0.8179
 
0.1%
189
 
0.1%
1.247
 
< 0.1%
1.434
 
< 0.1%
1.627
 
< 0.1%
1.819
 
< 0.1%
ValueCountFrequency (%)
11.61
< 0.1%
101
< 0.1%
9.21
< 0.1%
6.41
< 0.1%
6.21
< 0.1%
62
< 0.1%
5.82
< 0.1%
5.61
< 0.1%
5.22
< 0.1%
52
< 0.1%

direction_du_vecteur_de_vent_max_en_degres
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.901964
Minimum0
Maximum337.5
Zeros144360
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.873242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile225
Maximum337.5
Range337.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65.705858
Coefficient of variation (CV)2.9999985
Kurtosis7.3728472
Mean21.901964
Median Absolute Deviation (MAD)0
Skewness2.9522134
Sum3583665
Variance4317.2597
MonotonicityNot monotonic
2026-01-13T22:49:16.917358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0144360
88.2%
202.52730
 
1.7%
2252524
 
1.5%
247.52387
 
1.5%
2702039
 
1.2%
1801929
 
1.2%
157.51517
 
0.9%
67.51378
 
0.8%
451329
 
0.8%
90769
 
0.5%
Other values (6)2661
 
1.6%
ValueCountFrequency (%)
0144360
88.2%
22.5502
 
0.3%
451329
 
0.8%
67.51378
 
0.8%
90769
 
0.5%
112.5428
 
0.3%
135450
 
0.3%
157.51517
 
0.9%
1801929
 
1.2%
202.52730
 
1.7%
ValueCountFrequency (%)
337.5223
 
0.1%
315379
 
0.2%
292.5679
 
0.4%
2702039
1.2%
247.52387
1.5%
2252524
1.5%
202.52730
1.7%
1801929
1.2%
157.51517
0.9%
135450
 
0.3%

force_moyenne_du_vecteur_vent
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.45728901
Minimum0
Maximum9
Zeros120805
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:16.960051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95421864
Coefficient of variation (CV)2.0866861
Kurtosis8.8244561
Mean0.45728901
Median Absolute Deviation (MAD)0
Skewness2.7248236
Sum74823
Variance0.91053321
MonotonicityNot monotonic
2026-01-13T22:49:16.993731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0120805
73.8%
124615
 
15.0%
29924
 
6.1%
34723
 
2.9%
42257
 
1.4%
5818
 
0.5%
6328
 
0.2%
7121
 
0.1%
830
 
< 0.1%
92
 
< 0.1%
(Missing)20
 
< 0.1%
ValueCountFrequency (%)
0120805
73.8%
124615
 
15.0%
29924
 
6.1%
34723
 
2.9%
42257
 
1.4%
5818
 
0.5%
6328
 
0.2%
7121
 
0.1%
830
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
830
 
< 0.1%
7121
 
0.1%
6328
 
0.2%
5818
 
0.5%
42257
 
1.4%
34723
 
2.9%
29924
 
6.1%
124615
 
15.0%
0120805
73.8%

force_rafale_max
Real number (ℝ)

High correlation  Zeros 

Distinct27
Distinct (%)< 0.1%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.2397279
Minimum0
Maximum50
Zeros74100
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2026-01-13T22:49:17.163274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile13
Maximum50
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2877613
Coefficient of variation (CV)1.3234943
Kurtosis4.1981902
Mean3.2397279
Median Absolute Deviation (MAD)2
Skewness1.8350144
Sum530094
Variance18.384897
MonotonicityNot monotonic
2026-01-13T22:49:17.209273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
074100
45.3%
320937
 
12.8%
517188
 
10.5%
215299
 
9.3%
612475
 
7.6%
87585
 
4.6%
105617
 
3.4%
133378
 
2.1%
142284
 
1.4%
161563
 
1.0%
Other values (17)3197
 
2.0%
ValueCountFrequency (%)
074100
45.3%
215299
 
9.3%
320937
 
12.8%
517188
 
10.5%
612475
 
7.6%
87585
 
4.6%
105617
 
3.4%
11729
 
0.4%
133378
 
2.1%
142284
 
1.4%
ValueCountFrequency (%)
501
 
< 0.1%
401
 
< 0.1%
381
 
< 0.1%
374
 
< 0.1%
355
 
< 0.1%
344
 
< 0.1%
3221
< 0.1%
3016
 
< 0.1%
2930
< 0.1%
2750
< 0.1%

temperature_en_degre_c
Real number (ℝ)

High correlation 

Distinct457
Distinct (%)0.3%
Missing20
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.959987
Minimum-50
Maximum42.6
Zeros95
Zeros (%)0.1%
Negative3021
Negative (%)1.8%
Memory size1.2 MiB
2026-01-13T22:49:17.257810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-50
5-th percentile3.3
Q19.9
median15.1
Q320.9
95-th percentile29.2
Maximum42.6
Range92.6
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.33929
Coefficient of variation (CV)0.69112959
Kurtosis15.680339
Mean14.959987
Median Absolute Deviation (MAD)5.5
Skewness-2.6024654
Sum2447798
Variance106.90092
MonotonicityNot monotonic
2026-01-13T22:49:17.306810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-501871
 
1.1%
11.7964
 
0.6%
12951
 
0.6%
11.8947
 
0.6%
11.3945
 
0.6%
9.5907
 
0.6%
11.5904
 
0.6%
11.6887
 
0.5%
10.2878
 
0.5%
10.4850
 
0.5%
Other values (447)153519
93.8%
ValueCountFrequency (%)
-501871
1.1%
-4.912
 
< 0.1%
-4.814
 
< 0.1%
-4.710
 
< 0.1%
-4.62
 
< 0.1%
-4.43
 
< 0.1%
-3.929
 
< 0.1%
-3.823
 
< 0.1%
-3.724
 
< 0.1%
-3.619
 
< 0.1%
ValueCountFrequency (%)
42.61
< 0.1%
42.41
< 0.1%
42.21
< 0.1%
41.91
< 0.1%
41.82
< 0.1%
41.62
< 0.1%
41.52
< 0.1%
41.42
< 0.1%
41.32
< 0.1%
41.21
< 0.1%
Distinct163314
Distinct (%)99.8%
Missing20
Missing (%)< 0.1%
Memory size1.2 MiB
Minimum2019-06-06 02:00:00+02:00
Maximum2025-10-26 01:45:00+02:00
Invalid dates66622
Invalid dates (%)40.7%
2026-01-13T22:49:17.356326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:17.410367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct163314
Distinct (%)99.8%
Missing20
Missing (%)< 0.1%
Memory size1.2 MiB
Minimum2019-06-06 00:00:00+00:00
Maximum2025-11-06 08:45:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-13T22:49:17.465410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:17.519411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2026-01-13T22:49:14.407730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:08.813593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.416216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.023517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.610457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.333660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.951769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.520280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.233082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.813886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.460786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:08.874202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.472986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.077462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.666046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.390843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.005700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.580847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.292769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.871548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.521249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:08.935761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.535569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.139671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.735535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.452406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.062099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.647647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.353292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.929984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.574826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:08.992868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.591674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.193765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.792521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.515462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.119515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.707659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.408096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.991019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.633832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.053514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.661086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.251040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.858179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.579224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.175562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.773724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.465077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.051526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.693671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.109438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.725271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.312226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.920533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.644145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.233753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.835216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.527080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.111565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.748524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.169329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.784316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.375875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.981730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.702032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.296961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.894823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.582830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.171101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.811491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.234557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.843838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.435726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.159797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.765456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.356118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.960121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.640946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.229272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.871298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.296088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.903878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.498917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.216926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.831508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.410521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.019241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.695444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.286138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.933712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.352395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:09.962580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:10.553991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.275942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:11.889950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:12.466360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.171421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:13.751713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-13T22:49:14.341195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-13T22:49:17.566417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
direction_du_vecteur_de_vent_maxdirection_du_vecteur_de_vent_max_en_degresdirection_du_vecteur_vent_moyenforce_moyenne_du_vecteur_ventforce_rafale_maxhumiditeidpluiepluie_intensite_maxpressiontemperature_en_degre_c
direction_du_vecteur_de_vent_max1.0001.0000.7280.0730.291-0.1470.049-0.026-0.010-0.0730.182
direction_du_vecteur_de_vent_max_en_degres1.0001.0000.7280.0730.291-0.1470.049-0.026-0.010-0.0730.182
direction_du_vecteur_vent_moyen0.7280.7281.0000.0950.276-0.1690.020-0.013-0.009-0.0700.172
force_moyenne_du_vecteur_vent0.0730.0730.0951.0000.762-0.1130.019-0.0020.001-0.1660.080
force_rafale_max0.2910.2910.2760.7621.000-0.1400.0200.0060.014-0.1730.128
humidite-0.147-0.147-0.169-0.113-0.1401.0000.0630.1780.1990.120-0.648
id0.0490.0490.0200.0190.0200.0631.0000.0000.0000.1180.065
pluie-0.026-0.026-0.013-0.0020.0060.1780.0001.0000.911-0.110-0.092
pluie_intensite_max-0.010-0.010-0.0090.0010.0140.1990.0000.9111.000-0.115-0.101
pression-0.073-0.073-0.070-0.166-0.1730.1200.118-0.110-0.1151.000-0.170
temperature_en_degre_c0.1820.1820.1720.0800.128-0.6480.065-0.092-0.101-0.1701.000

Missing values

2026-01-13T22:49:15.037725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-13T22:49:15.182433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-13T22:49:15.613003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dataidhumiditedirection_du_vecteur_de_vent_maxpluie_intensite_maxpressiondirection_du_vecteur_vent_moyentype_de_stationpluiedirection_du_vecteur_de_vent_max_en_degresforce_moyenne_du_vecteur_ventforce_rafale_maxtemperature_en_degre_cheure_de_parisheure_utc
0552a630fca7800000c20000042.079.00.00.099700.00.0ISS0.00.00.00.013.22025-09-10T07:45:00+02:002025-09-10T05:45:00+00:00
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